Micro aerial vehicles (MAVs) and other robotic vehicles equipped with on-board sensors are ideal platforms for autonomous navigation in complex and confined environments for solving tasks such as exploration, inspection, mapping, interaction with the environment, search and rescue, and other similar applications. While MAVs are now available as off-the shelf products from a number of companies, most products rely on GPS, with vision and acoustic sensors used only for altitude stabilization.
However, recent research on MAVs has yielded a number of significant results. There are a number of Simultaneous Localization and Mapping (SLAM) approaches for MAVs. Good results have been obtained using monocular cameras and IMUs (inertial measurements units consisting of gyroscope and accelerometer), stereo camera configurations, and RGB-D sensor systems. In some implementations, for example, a Kinect and the vehicles onboard sensors are used to perform state estimation using an extended Kalman filter (EKF), while in other configurations, the same filter is used to combine monocular visual information with inertial sensor data to solve the scale factor problem. Despite these developments, though, these algorithms generally only work without limitation on laptop or desktop computers, which greatly curtails the range of possible implementations.
Moreover, RGB-D sensors have low quality cameras and suffer during exposure to direct sunlight. The minimal sensor suite for autonomous localization consists of two inexpensive, lightweight and widely available sensors, a single camera, and an IMU, including theoretical observability analysis and applied to aerial navigation. However, most solutions are heavy and over 1 kg in mass. Second, there is still a gap between the use of complex algorithms in the research field and its use by naive humans for everyday applications.